ArticleComputers, informatics, nursing : CIN2016
Utilizing Home Healthcare Electronic Health Records for Telehomecare Patients With Heart Failure: A Decision Tree Approach to Detect Associations With Rehospitalizations.
Article in Computers, informatics, nursing : CIN, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed.
- Community Health Nurses' Knowledge and Perceptions of AI in Canada: National Cross-Sectional Survey.JMIR nursing · 2026Article
- A Few-Shot Learning-Based Framework for Predicting Likelihood of In-patient's Mortality.Journal of healthcare informatics research · 2025Article
- Artificial Intelligence in the Intensive Care Unit: Current Evidence on an Inevitable Future Tool.Cureus · 2024Review
- Machine learning applied to electronic health record data in home healthcare: A scoping review.International journal of medical informatics · 2023Article
- Prediction model of quality of life using the decision tree model in older adult single-person households: a secondary data analysis.Frontiers in public health · 2023Article
- Machine learning decision tree models for multiclass classification of common malignant brain tumors using perfusion and spectroscopy MRI data.Frontiers in oncology · 2023Article
- Modern Approaches for the Treatment of Heart Failure: Recent Advances and Future Perspectives.Pharmaceutics · 2022Review
- Artificial Intelligence in Cardiovascular Medicine: Current Insights and Future Prospects.Vascular health and risk management · 2022Review
- Mobility and Self-Care are Associated With Discharge to Community After Home Health for People With Dementia.Journal of the American Medical Directors Association · 2021Article
- Application of machine learning in predicting hospital readmissions: a scoping review of the literature.BMC medical research methodology · 2021Article
- Function and Caregiver Support Associated With Readmissions During Home Health for Individuals With Dementia.Archives of physical medicine and rehabilitation · 2020Article
- Mining telemonitored physiological data and patient-reported outcomes of congestive heart failure patients.PloS one · 2018Observational
- Applying of Decision Tree Analysis to Risk Factors Associated with Pressure Ulcers in Long-Term Care Facilities.Healthcare informatics research · 2017Article
- Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques.Computational and structural biotechnology journal · 2017Review
- Identifying Targets to Improve Heart Failure Outcomes for Patients Receiving Home Healthcare Services: The Relationship of Functional Status and Pain.Home healthcare nowArticle
- Patient factors associated with the initiation of telehealth services among heart failure patients at home.Home health care services quarterlyArticle
- Identifying unmet needs of older adults transitioning from home health care to independence at home: A qualitative study.Geriatric nursing (New York, N.Y.)Article
- The Utility of Nursing Notes Among Medicare Patients With Heart Failure to Predict 30-Day Rehospitalization: A Pilot Study.The Journal of cardiovascular nursingArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Heart failure is a complex condition with a significant impact on patients' lives. A few studies have identified risk factors associated with rehospitalization among telehomecare patients with heart failure using logistic regression or survival analysis models. To date, there are no published studies that have used data mining techniques to detect associations with rehospitalizations among telehomecare patients with heart failure. This study is a secondary analysis of the home healthcare electronic medical record called the Outcome and Assessment Information Set-C for 552 telemonitored heart failure patients. Bivariate analyses using SAS and a decision tree technique using Waikato Environment for Knowledge Analysis were used. From the decision tree technique, the presence of skin issues was identified as the top predictor of rehospitalization that could be identified during the start of care assessment, followed by patient's living situation, patient's overall health status, severe pain experiences, frequency of activity-limiting pain, and total number of anticipated therapy visits combined. Examining risk factors for rehospitalization from the Outcome and Assessment Information Set-C database using a decision tree approach among a cohort of telehomecare patients provided a broad understanding of the characteristics of patients who are appropriate for the use of telehomecare or who need additional supports.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.